It probably doesn’t surprise you that the crypto biz has been booming lately—regardless of the ups and downs in the market—and it has meant nonstop travel to industry events for me. From Solana Breakpoint in Lisbon, to ETHDenver, to Avalanche Summit in Barcelona, to Harvard Blockchain Conference in Boston, I've been immersed with the builders, investors, and market watchers for months.
But it’s always been like this in crypto: Aside from an 18-month hiatus to get my masters degree from Harvard Business School, I’ve been doing the biz dev thing full-time since 2017. I’ve learned a lot—sometimes the hard way. Here are my keys to success for those already in the industry and those looking to get in. Crypto is chaos, but amid the chaos are principles one should follow in order to find success.
Lesson 1: Think like a VC
In the Web3 world, competition is especially dynamic and fierce. Superior technology, novel crypto-economic incentives, or macroeconomic conditions could easily dethrone existing winners. Operators need to stay vigilant about who’s doing what in the ecosystem (the popular founder claim “Our biggest competitor is ourselves” isn’t really good enough) while also remaining laser-focused on supporting their own product adoption.
Continue reading: https://decrypt.co/97391/3-keys-to-success-in-crypto-biz-dev

A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.
While tools exist to help experts make sense of a model’s reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.
Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model’s behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model’s reasoning matches that of a human.
Shared Interest could help a user easily uncover concerning trends in a model’s decision-making — for example, perhaps the model often becomes confused by distracting, irrelevant features, like background objects in photos. Aggregating these insights could help the user quickly and quantitatively determine whether a model is trustworthy and ready to be deployed in a real-world situation.
Continue reading: https://scitechdaily.com/do-humans-and-ai-think-alike/

It’s not new that the emerging artificial intelligence technology aims to take over the writing space.
High-end and intermediate writers have expressed cynical views and even fears over the AI writing software introduction.
For proponents of the AI writing application, it’s not so! According to them, the concept behind the creation is to help lessen the workload of writers.
In the meantime, the number of AIs has surpassed expectations. From small companies to big names in tech, AIs are attempting to become the next big thing for content marketing. 
In fact, due to the improvement in its machine language and data analytics, some companies prefer AI content marketing. 
This begs the question, is AI the future of writing? Or will it replace the human writing form?
Read on!
For a start, how does an Artificial intelligence Writing tool work?
If you’ve been wondering what goes on behind every AI, it’s simple, a machine language. 
AI writing tools use natural language generation to produce written words from mere data. You just input data in, and the rest is history. 
An AI is effective when a large amount of data needs conversion into written language that anyone can understand. 
Continue reading: https://www.rudebaguette.com/en/2022/04/is-artificial-intelligence-the-future-of-writing/

NEW DELHI : The pandemic has forced the retail sector to accelerate the pace of technology adoption. In 2021, a report by market research and advisory firm Forrester noted that India’s overall retail growth had shrunk for the first time amid lockdowns but it did not slow down the online retail sector that still grew at 5%, adding $1.6 billion in sales. Srini Venkatesan, executive vice president of US Omni Tech at Walmart Global Tech , has seen the use of technology in retail up close. In an interview, Venkatesan explained the role artificial intelligence (AI), augmented reality (AR) and other emerging technologies play in the retail sector. Edited excerpts:
How has India contributed to Walmart’s growth as a technology provider?
I have been involved in India for six years and have seen it grow from a small footprint. India has been an integral part of what we do. Our location in Bengaluru has some of the best talent pool in the world. A lot of our innovations in the last mile, fulfilment and forecasting have come from India.
How has the pandemic impacted the retail sector and what role has AI played in it?
The pandemic caused safety concerns for consumers. The needs of customers have changed because of it (too). Online pickup and delivery have flourished. AI has played a tremendous role in what we do, from offering delightful customer experiences to planning replenishment and flow of goods. AI has enabled us to know the inventory placement, where the demand is, and whether the inventory is reaching the right store for the audience.
Continue reading: https://www.livemint.com/companies/people/ai-can-help-personalize-offline-experiences-as-well-11649611527390.html

AI solutions development
In the 21st century, artificial intelligence for business is like the invention of electricity over a century ago. Nowadays, AI technologies are also revolutionizing the work of all sectors of the economy. The main thing is to properly implement AI in your business. Hundreds of businesses have failed, spending millions of dollars in the pursuit of innovation. Meanwhile, it is worth starting small to achieve great things. We’ll tell you how to do it.
The case at MD Anderson Cancer Center has shown that one should not overestimate one’s capabilities. The center relied on the IBM Watson system to help diagnose certain forms of cancer and prescribe treatments. The company did not consider its financial reserve. When the costs exceeded $62 million, the medical center closed the project. But the system was only at an experimental stage.
The example mentioned above shows that it is necessary to automate workflows gradually. You should assess your strengths and financial capabilities to realize what type of AI will help you to solve a specific business problem.
AI: five technologies in one
AI models the human mind to solve business problems. Calling a single application “artificial intelligence” is like calling Tesla a “vehicle”. You can’t go wrong technically, but you won’t cover the details. To solve a business problem, you need to distinguish the main types of AI.
Continue reading: https://www.crowdfundinsider.com/2022/04/189588-how-to-implement-ai-in-your-business-so-that-your-investment-in-this-technology-pays-off/

This is your opportunity to meet with other members of our group to network and share in conversations around today's top topics.
Everyone is welcome, but registration is required.
Register here: https://connect.comptia.org/events/registration?eventid=AWIT422

The month of May is Asian American Pacific Islanders (AAPI) Heritage Month. Their contributions to STEM tend to go unnoticed.  Did you know that Zoom was invented by Eric Yuan, a former Cisco engineer, born and raised in Tai’an, Shandong Province, China?  Check out 8 other Groundbreaking Contributions by Asian Americans Through History - From disease cures to influential tech to workers' rights, Asian American innovators have made huge impacts on people's lives. https://www.history.com/news/asian-american-inventions-contributions
 
Photo by Mart Production 

A company in Texas "Rickrolled" an entire city using 300 drones to create a gigantic QR code in the sky.
Sky Elements Drones Shows, based in Fort Worth, launched the drones 121 meters (400 feet) up in Dallas on April Fool's day as a prank. Anybody curious enough to see a QR code light up the city sky and scan it was treated to a Rickroll. Rickrolling, for those who have missed out on pretty much the entire history of the Internet, is where you trick people into watching a short clip of Rick Astley singing Never Gonna Give You Up. For a full definition see here.
Continue reading: https://www.iflscience.com/technology/300-drones-just-rickrolled-an-entire-city-in-texas/

IoT Analytics, a leading global provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, today released the 2022 Emerging Technologies Radar for IoT Projects.
The analyst team at IoT Analytics handpicked 58 of the most promising technologies relevant to IoT projects globally and ranked them according to their perceived maturity.
The resulting Emerging IoT Technologies Radar will help anyone working in IoT-type environments and projects understand which technologies they should be watching, evaluating, and perhaps deploying. The full report is available to IoT Analytics corporate subscription clients here: Emerging IoT Technologies Report 2022. The report contains additional details, such as market statistics, major vendors, and recent trends, for each of the highlighted IoT technologies, which are anywhere between “coming up” and “mainstream.”
Key quotes:

• Knud Lasse Lueth, CEO at IoT Analytics says:
  

  “Many of the technologies available to IoT practitioners have matured significantly in the last 3 years. That is why we are currently witnessing increased adoption of IoT across the board and more and more projects that are scaling up. Nonetheless, there is still so much technology on the horizon which, once more mature, will mean another step-change for the Internet of Things.”

  Click to expand...

• Satyajit Sinha, Senior Analyst at IoT Analytics adds: “Wi-Fi 6, GPUs, and smart sensors were the fastest adaptive technologies in the last three years. The quick deployment of hardware components, expansion to new applications, and extensive updates on existing technology were key factors for higher adoption.”

Selected highlights:
IoT Software. Eight IoT technologies are nearing maturity, including edge AI, IoT-based streaming analytics, and supervised and unsupervised machine learning.
Continue reading: https://iotbusinessnews.com/2022/04/06/64603-55-emerging-iot-technologies-you-should-have-on-your-radar/

Since the beginning of the COVID-19 pandemic, the challenges facing supply chain workforce networks have been highlighted more than ever before as industries struggle to meet demand.
Pandemic challenges in the workforce
The COVID-19 pandemic saw businesses struggling worldwide to keep up with demand and a declining workforce. Supply chain workforce networks worldwide struggled to maintain employees as more and more people stayed home or opted for remote positions.
Access to raw materials decreased, and worker shortages led to inventory reductions and price inflation. These challenges push supply chains to adopt new technologies and diversify their leadership to become more efficient and reliable in unpredictable times.
Corporate accessibility for women
Seagull Scientific reports that “incorporating diverse perspectives (including those from women) into complex workforces, especially within executive or leadership teams, can lead to above-average profits.”
Despite this trend, however, most women in the supply chain industry hold first-line and supervisor roles, with the lowest percentage of women in executive and corporate positions.
Women in supply chain workforces worldwide are being lost at mid-career levels due to a lack of opportunity. Trends show, however, that companies that hire more women in leadership roles are more efficient and diverse.
The push for DEI initiatives across industries in the last couple of years has seen more women in supply chain organisations than ever before and more of those women holding leadership positions.
Hiring a diverse team can bring new perspectives and ideas to a business, leading to creative problem solving and success. Gender equality in the workplace should be a priority for supply chain businesses in 2022.
Continue reading: https://www.openaccessgovernment.org/women-supply-chain-industry%E2%80%AF-gender-equality-digital-transformation-technology/133397/

As the velocity of technological innovation has increased, the Internet of Things (IoT) has gained tremendous momentum. IoT devices worldwide are projected to amount to 30.9 billion units by 2025, which is a huge jump from 2021. The proliferation of IoT has dramatically impacted daily life. From your smart coffee maker to smart lights, IoT controls a vast amount of our routines and data. It doesn’t end on a personal level either. Nearly every industry uses IoT devices to enhance efficiency, productivity, safety, resource conservation, and security. Custom IoT software will become a necessity for insight into data analytics.
The vast network of interconnected devices collect, send, and exchange enormous amounts of data over networks. Wide-spread IoT solutions power our “smart” homes, phones, cars, wearables, mobile e-commerce, smart grids, and Industrial IoT. The sky is the limit in IoT software innovation.
What Is Custom IoT Software?
Industries in every sector recognize the enormous value of using IoT. IoT solutions streamline operations and facilitate agile business models. According to GlobeData, the IoT market revenue is expected to reach $1 trillion globally by 2024. Hence, there is a rising demand for IoT-oriented software engineering. Healthcare, retail, automotive, manufacturing, financial, hospitality, and many other industries benefit from the adoption of IoT solutions and software.
Custom IoT software is invaluable for providing visibility and insight into products using data metrics and analytics. Cutting-edge IoT software can increase ROI while optimizing operations and delivering an improved customer experience with intuitive GUIs. The derived data analytics and monitoring combine to enhance safety, improve healthcare delivery, monitor critical utility infrastructures, and much more. According to Gigabit, companies recognize the need for custom IoT software and could invest up to $15 trillion in IoT software solutions by 2025. Well-designed IoT software maximizes a company’s investments and growth potential.
Continue reading: https://www.iotforall.com/why-custom-software-for-iot-solutions-is-a-growing-necessity-and-expectation

In one corner of the AI/ML world, NVIDIA does its best to convince anyone who wants to dip their toes into training that more raw power is the answer. In the other, CEA-Leti recently announced that Elisa Vianello, an Edge AI program coordinator, nabbed a €3 million grant from the European Research Council (ERC) to develop new edge AI systems inspired by insect nervous systems.
According to Vianello, one of the biggest challenges around bringing AI directly to IoT devices, which would allow them to make autonomous decisions based on comparing sensor input with trained data and decision trees, is that the current chip architectures waste up to 90% of their total energy consumption on moving data around, not processing it.
See also: Researchers Surpass Speech Recognition Methods with Help From Honey Bees
Because of this waste, IoT devices are either hindered in their AI capabilities or have to be physically tethered to a stable supply of power, which means they’re not nearly as flexible as many organizations would like. Doubly so for one proposed application, implantable medical diagnostic microchips, which would rely heavily on the user’s trust in the device’s reliability.
What’s the hiccup when it comes to small-scale devices? There just isn’t a type of memory that’s high-density, high-resolution, non-volatile, and endlessly endurant. Vianello says many industry labs and research centers have tried developing in-memory architectures in nano-scale, which use in-memory processing, but the results have been mixed at best. DRAM, for example, is volatile, which means that its contents are deleted when power is lost—a likely occurrence in many IoT settings. Non-volatile memory types, such as NVRAM, have dramatically improved endurances over the years; they’re still not completely infallible.
Vianello and her team will use the grant funding to research how the functions of specific insect nervous systems resemble the functions performed by deterministic, probabilistic, volatile, and non-volatile memory and then explore how those could be recreated in “high-performance, energy-efficient, silicon-based nanosystems.” Vianello says, “Crickets make accurate decisions based on sluggish, imprecise, and unreliable neurons and synapses in order to escape their predators. Looking closely at their biology, we identified a diversity of memory-like functions at play in their sensory and nervous systems. By combining these different functions, the cricket’s internal computing system achieves amazing performance and energy efficiency.”
For example, crickets have several sensors on their body, along with numerous local processing units in their abdomen, capable of continuous learning and decision-making without involving the central brain. Because it has a distributed computing system, it makes decisions faster—without having to transfer data from one place to another before processing it.
Continue reading: https://www.rtinsights.com/are-bugs-the-future-of-ai-in-the-internet-of-things/

Internet of things (IoT) broadly means “All objects, which are connected to the web, will be enabled to share and process data through their sensors and communication devices independently”. It is estimated that by 2023, globally there may be 7 billion connected people, more than 30 million applications and embedded systems, more than 60 trillion GBs of data and around 8 trillion revenue opportunity. The IoT market value is expected to reach $11 trillion with security spending reaching $3.5 billion shortly.
The history of IoT evolution started with archaic mode of interaction between human and thing (people and things), followed by interaction of machine and machine (things and things), leading to interaction of people kicking off through proverbial internet (people and people), resulting in multiple things getting connected on net (web of things) and then finally the internet of humanity.
IoT in agriculture basically involves the network wherein physical components in the sector is to be connected with internet which may be the farms comprising trees, plants, and animals, the tools and various objects in the sector. This enables information exchange, monitoring and tracking, helping humans manage complex agricultural sector more productively under certain protocols. For example, most of the developed countries such as Germany, Japan and US has already moved ahead in sensor technology and manufacturing processes. The net benefit of IoT application is the increased agricultural
output, improved quality of agricultural products, reduced labour costs coupled with increased agricultural income for farmers.
Continue reading: https://www.business-standard.com/article/technology/internet-of-things-may-revolutionise-the-agricultural-sector-122040700216_1.html

Recently, an increasing amount of hope is attached to edge computing. The industry is buzzing with bold ideas such as “the edge will eat the cloud” and real-time automation will spread across healthcare, retail, and manufacturing.
Experts agree that edge computing will play a key role in the digital transformation of almost every business. But progress has been slow. Legacy perception has held companies back from fully leveraging the edge for real-time decision-making and resource allocation. To understand how and why this is happening, let’s look back at the first wave of edge computing and what has transpired since then.
The first wave of edge computing: Internet of Things (IoT)
For most industries, the idea of the edge has been tightly associated with the first wave of the Internet of Things (IoT). At the time, much of the focus centered around collecting data from small sensors affixed to everything and then transporting that data to a central location – like the cloud or main data center.
These data flows then had to be correlated into what is commonly referred to as sensor-fusion. At the time, sensor economies, battery lifetime, and pervasiveness often resulted in data streams that were too limited and had low fidelity. In addition, retrofitting existing equipment with sensors was often cost prohibitive. While the sensors themselves were inexpensive, the installation was time consuming and required trained personnel to perform. Finally, the expertise needed to analyze data using sensor-fusion was embedded in the knowledge base of the workforce across organizations. This led to slowing adoption rates of IoT.
Additionally, security concerns cooled wholesale adoption of IoT. The math is as simple as this:  thousands of connected devices across multiple locations equals a large and often unknown exposure.  As the potential risk outweighed the unproven benefits, many felt it was prudent to take a wait-and-see attitude.
Moving beyond IoT 1.0
It is now becoming clear the edge is less about an IoT and more about making real-time decisions across operations with distributed sites and geographies. In IT and increasingly in industrial settings, we refer to these distributed data sources as the edge. We refer to decision-making from all those locations outside the data center or cloud as edge computing.
The edge is everywhere we are — everywhere we live, everywhere we work, everywhere human activity takes place. Sparse sensor coverage has been solved with newer and more flexible sensors. New assets and technology come with a wide array of integrated sensors. And now, sensors are often augmented with high resolution/high fidelity imaging (x-ray equipment, lidar).  
Continue reading: https://www.cio.com/article/308159/edge-computing-is-thriving-in-the-cloud-era.html

Bring IT On brings together tech professionals to inspire the next generation of young women to enter the sector
The Bring IT On team returned with their ‘Girls in Tech’ event this year with a sold-out event at the Clayton Hotel, Belfast. Dedicated to female school students from across Northern Ireland, ‘Girls in Tech’ aims to overcome stereotypes and inspire young women to consider a career in the tech sector.
Attended by 130 pupils and teachers from 13 schools across Northern Ireland, this year’s event was an immense success thanks to the fantastic line up of professionals who kept the audience engaged. These professional women who work in the Digital IT sector inspired the young women with interactive sessions that explored the many reasons why they should consider a career in Tech.
Speakers included Sapphire Duffy, Global Community Manager at Women Who Code, who explained in detail her own career journey into the tech sector. The school pupils were eager to find out more and were very open to discovering as much as they could about the many career opportunities open to women.
Continue reading: https://www.fenews.co.uk/skills/tech-professionals-inspire-the-next-generation-of-young-women-to-work-in-it/

I’ve been a data practitioner responsible for the delivery of data management strategies in financial services, online retail, and just about everything in between. In all of these roles, I’ve come across patterns that enable organizations to build faster business insights and innovation with data.
These patterns encompass a way to deliver value to the business with data; I refer to them collectively as the “data operating model.” It facilitates the alignment of people, processes, and technology toward a common vision and objective. Organizational outcomes such as being data-driven, data democratization, automation, self-service, developer velocity, and delivering faster insights and increased revenue can all result from the efficiency that a data operating model engenders.
These outcomes are attractive, but for practitioners like you, execution is where the rubber hits the road. In this article, I’ll explore the three execution patterns I’ve come across that have engendered success with data: cloud-native technologies, real-time data, and open source software.
Execution patterns in an operating model
If, as Gartner puts it, an operating model brings the broader business model to life, then execution patterns are an important part of breathing life into an operating model. Patterns maintain consistency when executing on the operating model. Mike Tyson is often quoted as saying, “Everyone has a plan until they get punched in the mouth.” 
Similarly, an operating model can be challenged when there are changes in leadership, architects, technical leaders, developers, product managers or new additions to a technology stack. But established execution patterns help the operating model, strategy, and vision stay on track. They’re also an excellent aid to bringing on new team members quickly.
1) The cloud-native pattern
The first execution pattern is cloud-native. Cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives by 2025 — up from less than 40% in 2021. Why are enterprises shifting to the cloud? They’re trying to leverage the benefits of the private, hybrid, or public cloud. Lower total cost of ownership, scalable unit economics, multi-region reliability, digital transformation, faster delivery of applications, and machine learning models—these are all business benefits of cloud-native adoption.  
Communicating the business value of cloud-native adoption is an important part of this pattern.   Cloud-native is much more than cloud, Kubernetes, services, CI/CD, and automation. In the context of applications and data, creating and maintaining a cloud-native strategy provides portability, resilience, fault tolerance, scalability, and flexibility. A cloud-native pattern helps manage the costs and resources of the technology stack for the business in a consistent way.
Speed helps drive innovation. The faster applications can be deployed, data can be integrated and refined, different algorithms and data sets can be tested for new models, the faster business can make new decisions. A cloud-native pattern helps reduce barriers to innovation, supports frictionless change, and enables innovation with data to happen faster.   
2) The real-time data pattern
The ability to assess data in real-time is set to be one of the biggest data analysis trends for 2022. According to Gartner, more than 50% of new business systems will use real-time data to improve decision-making by 2022. Making decisions faster in real-time with trusted data leads to a competitive advantage.
Continue reading: https://www.cio.com/article/308122/3-patterns-for-business-success-with-data.html

In this new financial landscape, where fintech is reshaping the sector, blockchain development companies have a leg up. The adoption of the new economic model by users will have a major impact on the rate and scope of this transition. It's clear that the public is fed up with black boxes and wants control over how their data and money are transferred.
Blockchain is a technology that makes it almost hard to change or break into a system by allowing the recording of information in a very secure manner. 
Digital ledgers, termed blocks, are used to keep track of transactions and assets in a business network using blockchain. One of the most appealing features of this technology is its decentralized ownership, which is well-known for democratizing processes, while also providing security, transparency, and efficiency.
Transactions between two persons or the tracking of the custody of couriers may be carried out using blockchain.
The platforms, which were built to provide "accurate reporting, monitoring, and analysis of all forms of digital financial transactions", are an intriguing use of blockchain technology. One of the examples is Bitcode AI, which allows beginner crypto investors to make the most out of their trading process. In addition, this AI-powered tool is practical for those who are afraid of losing money. The tool might be useful for individuals who want to generate a strategy suitable for their interests, as well. 
Continue reading: https://www.finextra.com/blogposting/22108/how-and-why-fintech-embraces-blockchain

Blockchain technology has made a significant position in the world of technology lately and is one of the most remarkable technological advancements. But skepticisms never leave a chance to hover around new and emerging techs. There have been controversial discussions regarding blockchain tech facing constant malicious activities. 
Blockchains are secured through a mass of varied consensus mechanisms, but virtually all share the same underlying security protocol called cryptography. Like Bitcoin works through mining, it is how transactions are confirmed on the network and are critical for the maintenance and development of blockchain ledgers. The process of altering the shared ledger with a newly added transaction in which all the core supporters synchronize the copies is what we call cryptocurrency mining. These core supporters are known as miners. BTC uses an algorithm known as SHA 256. 
But Despite the early record of security, blockchain technology has experienced several hacks and frauds, which raises questions as to whether the technology is reliable and future-proof. For instance, the Poly Network Hack was one of the biggest that witnessed around $600 Million stolen. 
Continue reading: https://www.thecoinrepublic.com/2022/04/06/can-we-trust-the-security-of-blockchain-tech/

For several months, consumers and businesses have been hearing more and more about blockchain technology, cryptocurrencies and, most recently, nonfungible tokens. All these concepts are among the first wave of what insiders have dubbed Web3—the next phase of the internet.
Broadly speaking, Web3 primarily differs from the current iteration of the internet in its focus on decentralization and transparency. Proponents tout a new normal in which users maintain more control of their data and businesses and organizations enjoy more seamless, direct interactions.
While Web2 may still be the standard, many tech experts are adamant that Web3 is on its way and will soon revolutionize the way consumers and companies digitally interact and conduct business. Here, 12 members of Forbes Technology Council share details about Web3 they say every consumer and business needs to know.
1. The Goal Is Decentralization Through Blockchain Technology
Web3 is primarily a small but loud social movement to decentralize today’s internet platforms and services using blockchain technology. In real-world applications of blockchain, the goal is to remove complex third-party verification processes. For example, the blockchain would transparently verify trades of complex securities such as shares in a power plant or trades of securities that are executable software. - Steven Gustafson, Noonum, Inc.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/04/06/tech-experts-share-12-things-businesses-and-consumers-need-to-know-about-web3/?sh=59de182df1b8

They call it Jenny; a robotic solution with Artificial Intelligence, AI capabilities.
Jenny has come to alter the status quo, disrupting the traditional method of multi-level marketing.
With its AI capabilities, Jenny eliminates the degrading methods of public sharing of printed literature and the beggarly system of selling supplements and other health-based products in the market, at bus stops and on the streets.
Built by Strategic Business Techspace for Wealth Solution Dynasty, WSD, Jenny has given a new impetus to multi-level marketing and handed WSD the bragging right as the first tech-based MLM outfit.
The chatbox AI solution is innovative and multitasks as a marketer and customer relations interface between WSD and its public.
It is sitting on the company’s social media handles- WhatsApp, Facebook, Instagram, Twitter and even on its website.
Continue reading: https://www.vanguardngr.com/2022/04/how-artificial-intelligence-revolutionizes-multi-level-marketing/

Artificial intelligence (AI) is rapidly advancing across sectors and industries and while it has great potential to benefit society, this can only be realized if AI truly represents the diversity of the populations it represents.
Gender equity, specifically, is not currently realized in AI development. A technology meant to replicate human functions learns from and relies on the data and teams that put it together and manage it. A lack of representation from women in these spaces creates bias and can make technology untrustworthy.
Many companies and organizations are looking to increase gender equity within their AI teams to deliver value. While there is much more work to be done, there are more women than ever in leadership roles paving the way in AI.
Here are three women working to bridge the gender equity gap when it comes to AI.
Karine Perset heads the AI Unit of the OECD Division for Digital Economy Policy. She oversees the OECD.AI Policy Observatory and OECD.AI Network of Experts (ONE AI), as well as the newly forming OECD Working Party on AI Governance (AIGO). Perset focuses on opportunities and challenges that AI raises for public policy, on policies to help implement the OECD AI Principles and on trends in AI development.
Continue reading: https://www.weforum.org/agenda/2022/04/women-in-ai-gender-equity-gap/

Employees are quitting jobs at record rates and companies are having a hard time luring them back. Exacerbating the problem is the fact that employees are now frequently working from home, making it harder for managers to identify employees who are unhappy. Plus, getting new hires up to speed is more challenging when they can’t attend in-person training sessions or shadow experienced employees.
To solve all these issues, companies are increasingly turning to artificial intelligence. But there’s a limit to how much AI can do.
Using AI to gauge employee sentiment
The “Great Resignation” has been no exaggeration. The number of people quitting their jobs has been unprecedented throughout the pandemic, hitting a record high of 48 million people in 2020, up from 42 million in 2019, which was the previous record high.
Unsustainable work performance expectations, a lack of career development and advancement potential, and a lack of meaningful work are among the top reasons motivating employees to move on, according to a March McKinsey survey, with inadequate compensation further down on the list, in sixth place. The No. 1 reason why people quit their jobs? Uncaring leaders.
The problem is that it can be hard to notice that employees are struggling when they’re suddenly all working remotely.
“I’m an old-school person and I do believe that there’s no substitute for managing by walking around,” says Bill Nowacki, decision science lead at KPMG. “I don’t think there’s a substitute for good managers knowing that your daughter had a soccer match last week.”
Continue reading: https://www.cio.com/article/307980/ai-takes-aim-at-employee-turnover.html

One of the best parts of being Forrester’s CEO is that I have hundreds of analysts who help me understand new technology. So I thought I would share the wealth and pass on some of that knowledge to my fellow CEOs. I will be putting out a post about every quarter focusing on a technology that has three characteristics: 1) in the news; 2) frequently misunderstood; and 3) CEOs should understand it. First stop … artificial intelligence.
What Is AI?
It’s using computers to detect patterns and make predictions. Example: using a software program to identify which of your customers are not going to renew.
Today’s AI is decidedly unsexy — it’s not about killer robots, computers curing cancer, or runaway autonomous vehicles. If the AI application your company is building might show up in a Hollywood movie or amaze a layperson, it is likely doomed to failure and will be a titanic waste of time and money. True applications are quietly valuable, modest, and they won’t impress your mother.
How Does AI Work?
AI is just another computer program. Step one: Obtain a relevant, clean, and well-formatted data set. Step two: Feed the data to machine-learning algorithms to build and train a model (formulas). Step three: Use the model to predict and find patterns.
Confused? Here’s an example …
Like any good CEO, you don’t want to lose customers. So you go to your CIO and say, “Build an AI system that will predict which of our customers are going to churn.” Here’s what they will do:
Step one: They will gather up data on customers from last year and put that data in a consistent form. Think an Excel spreadsheet with the first row as “Customer 1” with columns showing information that will be relevant to whether they reupped with you or not — things like “tenure,” “engagement,” or “dollars spent.”
Continue reading: https://www.forbes.com/sites/forrester/2022/04/06/artificial-intelligence-for-ceos/?sh=2a897af8f4e3

Almost everywhere I look on the internet today, I see the terms Internet of Things and Edge Computing. At first glance, these terms seem synonymous because they’re often deployed in the same infrastructure. If you look closely, though, you’ll realize they’re worlds apart.  
IoT and Edge both represent a paradigm shift in data gathering and analysis. These technologies also convert items you use daily into ‘smart’ devices. In turn, clocks, watches, and other home appliances are able to transmit data to another device/storage. Undoubtedly, this multi-device connection opens a world of opportunities.
While IoT and Edge have similar eventual outcomes, they’re very different. To understand their differences, we’ll need to take a closer look at each technology on its own. 
What Is the IoT?
The Internet of Things (IoT) is an ecosystem of interconnected objects. This ecosystem consists of physical, digital, mechanical, electrical, and computing devices. Each of these devices have Unique Identifiers (UIDs), and through them, the devices communicate with each other on a connected network. The highlight? These interactions don’t need human intervention.

IoT: A Connected World.
Let’s go over the IoT with 3 examples. 
1. Home Automation
Home automation is a key IoT application. You can connect your everyday gadgets like thermostats, fans, and lights to the IoT ecosystem. This enables you to turn them ON/OFF from a distance. 
Continue reading: https://techgenix.com/iot-vs-edge-computing/

Let’s say that your job is to monitor oil well operations across a small country. You have a device installed at every oil pumpjack, the mechanism that pumps oil out of the ground from an existing well. This device monitors local weather and pump operations. It even automates local processes on the pumpjack.
Collectively, these devices are known as edge computers. They have their own processors, local storage systems, operating systems, and networking interfaces that allow them to communicate with a centralized collection and analysis system. This centralized system uses artificial intelligence and data analytics to determine when human operators need to be dispatched. For instance, the device can determine when a pump motor is about to fail or when oil flow is too high or too low.
The devices also leverage centralized data collection to monitor overall production and provide oversight for all the pumpjacks producing oil. There are 500 devices in this specific edge computing network, one for each remote pumpjack, and all devices communicate back to a centralized system on a public cloud provider.
The first few months of using these edge computing devices to monitor remote and unmanned pumping operations went fine. However, storage systems on the devices soon began to fail due to a known flaw, and network interfaces stopped and had to be reset. Most often, some key sensors used on the pumpjack stopped working. These problems could only be fixed by sending out humans to fix them, thus incurring a cost that defeated the purpose of leveraging these devices to automate pump operations.
To address this problem, remember that we should deal with edge computing like any other compute and storage platform under operational control—do the basics. Back up the data on each edge device, remote to central. Remotely update the operating systems and firmware much like you do on a smartphone. Support application updates that include changes to the data structure. Also, track the configuration, including operating system releases, application updates and patches, and even the software versions running on some of the smart sensors.
Continue reading: https://www.infoworld.com/article/3655641/where-edge-computing-breaks-down-operations.html